Overview

Dataset statistics

Number of variables15
Number of observations403
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory47.4 KiB
Average record size in memory120.3 B

Variable types

Numeric10
Categorical3
Boolean2

Warnings

ReclameAqui has constant value "True" Constant
Volume_7days is highly correlated with Volume_15daysHigh correlation
Volume_15days is highly correlated with Volume_7days and 1 other fieldsHigh correlation
Volume_Monthly is highly correlated with Volume_15daysHigh correlation
%Insatisfação(CSAT) is highly correlated with CSAT_RatedHigh correlation
CSAT_Rated is highly correlated with %Insatisfação(CSAT)High correlation
Volume_7days is highly correlated with Volume_15daysHigh correlation
Volume_15days is highly correlated with Volume_7days and 1 other fieldsHigh correlation
Volume_Monthly is highly correlated with Volume_15days and 1 other fieldsHigh correlation
Tempo_Medio_Chat is highly correlated with Tempo_Medio_Email and 1 other fieldsHigh correlation
Tempo_Medio_Email is highly correlated with Tempo_Medio_Chat and 1 other fieldsHigh correlation
AWT_Chat is highly correlated with Tempo_Medio_Chat and 1 other fieldsHigh correlation
%Insatisfação(CSAT) is highly correlated with CSAT_RatedHigh correlation
CSAT_Rated is highly correlated with %Insatisfação(CSAT)High correlation
Target is highly correlated with Volume_MonthlyHigh correlation
Volume_15days is highly correlated with Volume_MonthlyHigh correlation
Volume_Monthly is highly correlated with Volume_15daysHigh correlation
%Insatisfação(CSAT) is highly correlated with CSAT_RatedHigh correlation
CSAT_Rated is highly correlated with %Insatisfação(CSAT)High correlation
Requester_ID is highly correlated with df_indexHigh correlation
%Insatisfação(CSAT) is highly correlated with CSAT_RatedHigh correlation
%NFCR is highly correlated with Volume_15days and 3 other fieldsHigh correlation
Volume_15days is highly correlated with %NFCR and 3 other fieldsHigh correlation
Target is highly correlated with %NFCR and 3 other fieldsHigh correlation
Volume_7days is highly correlated with %NFCR and 4 other fieldsHigh correlation
Month is highly correlated with df_indexHigh correlation
CSAT_Rated is highly correlated with %Insatisfação(CSAT) and 2 other fieldsHigh correlation
Volume_Monthly is highly correlated with %NFCR and 4 other fieldsHigh correlation
df_index is highly correlated with Requester_ID and 1 other fieldsHigh correlation
%Insatisfação(CSAT) is highly correlated with ReclameAquiHigh correlation
Month is highly correlated with ReclameAquiHigh correlation
SocialMedia is highly correlated with ReclameAquiHigh correlation
ReclameAqui is highly correlated with %Insatisfação(CSAT) and 3 other fieldsHigh correlation
Target is highly correlated with ReclameAquiHigh correlation
df_index has unique values Unique
Volume_7days has 220 (54.6%) zeros Zeros
Volume_15days has 80 (19.9%) zeros Zeros
Tempo_Medio_Chat has 132 (32.8%) zeros Zeros
Tempo_Medio_Email has 224 (55.6%) zeros Zeros
AWT_Chat has 132 (32.8%) zeros Zeros
%NFCR has 233 (57.8%) zeros Zeros
CSAT_Rated has 247 (61.3%) zeros Zeros

Reproduction

Analysis started2021-06-15 18:52:05.079338
Analysis finished2021-06-15 18:52:33.894938
Duration28.82 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct403
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78666.15136
Minimum96
Maximum152133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2021-06-15T15:52:34.013092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum96
5-th percentile9043.1
Q144557
median81852
Q3112213.5
95-th percentile140830.4
Maximum152133
Range152037
Interquartile range (IQR)67656.5

Descriptive statistics

Standard deviation41397.42472
Coefficient of variation (CV)0.5262418969
Kurtosis-1.070310983
Mean78666.15136
Median Absolute Deviation (MAD)32618
Skewness-0.1493294802
Sum31702459
Variance1713746773
MonotonicityStrictly increasing
2021-06-15T15:52:34.184769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450501
 
0.2%
424681
 
0.2%
853491
 
0.2%
986621
 
0.2%
1109611
 
0.2%
1375951
 
0.2%
1068721
 
0.2%
44731
 
0.2%
350311
 
0.2%
464161
 
0.2%
Other values (393)393
97.5%
ValueCountFrequency (%)
961
0.2%
8111
0.2%
21341
0.2%
23671
0.2%
23981
0.2%
32871
0.2%
41761
0.2%
44731
0.2%
46741
0.2%
49251
0.2%
ValueCountFrequency (%)
1521331
0.2%
1518801
0.2%
1517391
0.2%
1512141
0.2%
1510611
0.2%
1508121
0.2%
1505941
0.2%
1493811
0.2%
1481661
0.2%
1466261
0.2%

Requester_ID
Real number (ℝ≥0)

HIGH CORRELATION

Distinct395
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8486860.722
Minimum30084
Maximum19066870
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2021-06-15T15:52:34.372862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum30084
5-th percentile490402.6
Q13584947
median7661219
Q313832214
95-th percentile17230263.9
Maximum19066870
Range19036786
Interquartile range (IQR)10247267

Descriptive statistics

Standard deviation5656256.062
Coefficient of variation (CV)0.6664721205
Kurtosis-1.224239857
Mean8486860.722
Median Absolute Deviation (MAD)4817037
Skewness0.2044813836
Sum3420204871
Variance3.199323264 × 1013
MonotonicityNot monotonic
2021-06-15T15:52:34.567353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
138875003
 
0.7%
48409612
 
0.5%
185702942
 
0.5%
166527352
 
0.5%
5138112
 
0.5%
9976732
 
0.5%
119298862
 
0.5%
83661641
 
0.2%
31409821
 
0.2%
6649331
 
0.2%
Other values (385)385
95.5%
ValueCountFrequency (%)
300841
0.2%
562341
0.2%
580581
0.2%
810431
0.2%
993671
0.2%
1467951
0.2%
1488751
0.2%
1602361
0.2%
1769111
0.2%
1870851
0.2%
ValueCountFrequency (%)
190668701
0.2%
189340001
0.2%
188453441
0.2%
188381441
0.2%
188144671
0.2%
186857711
0.2%
186851621
0.2%
186559611
0.2%
186402651
0.2%
185832051
0.2%

Month
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
2021_02
121 
2021_05
113 
2021_04
96 
2021_01
73 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters2821
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021_01
2nd row2021_01
3rd row2021_01
4th row2021_01
5th row2021_01

Common Values

ValueCountFrequency (%)
2021_02121
30.0%
2021_05113
28.0%
2021_0496
23.8%
2021_0173
18.1%

Length

2021-06-15T15:52:35.021200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-15T15:52:35.170844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2021_02121
30.0%
2021_05113
28.0%
2021_0496
23.8%
2021_0173
18.1%

Most occurring characters

ValueCountFrequency (%)
2927
32.9%
0806
28.6%
1476
16.9%
_403
14.3%
5113
 
4.0%
496
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2418
85.7%
Connector Punctuation403
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2927
38.3%
0806
33.3%
1476
19.7%
5113
 
4.7%
496
 
4.0%
Connector Punctuation
ValueCountFrequency (%)
_403
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2821
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2927
32.9%
0806
28.6%
1476
16.9%
_403
14.3%
5113
 
4.0%
496
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2821
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2927
32.9%
0806
28.6%
1476
16.9%
_403
14.3%
5113
 
4.0%
496
 
3.4%

Volume_7days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7245657568
Minimum0
Maximum5
Zeros220
Zeros (%)54.6%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2021-06-15T15:52:35.322418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.029933581
Coefficient of variation (CV)1.421449429
Kurtosis3.607784779
Mean0.7245657568
Median Absolute Deviation (MAD)0
Skewness1.807225269
Sum292
Variance1.060763182
MonotonicityNot monotonic
2021-06-15T15:52:35.449755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0220
54.6%
1117
29.0%
239
 
9.7%
316
 
4.0%
46
 
1.5%
55
 
1.2%
ValueCountFrequency (%)
0220
54.6%
1117
29.0%
239
 
9.7%
316
 
4.0%
46
 
1.5%
55
 
1.2%
ValueCountFrequency (%)
55
 
1.2%
46
 
1.5%
316
 
4.0%
239
 
9.7%
1117
29.0%
0220
54.6%

Volume_15days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.369727047
Minimum0
Maximum6
Zeros80
Zeros (%)19.9%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2021-06-15T15:52:35.569982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.194838266
Coefficient of variation (CV)0.8723185169
Kurtosis1.542721277
Mean1.369727047
Median Absolute Deviation (MAD)1
Skewness1.278068591
Sum552
Variance1.427638483
MonotonicityNot monotonic
2021-06-15T15:52:35.686158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1199
49.4%
080
19.9%
260
 
14.9%
336
 
8.9%
416
 
4.0%
511
 
2.7%
61
 
0.2%
ValueCountFrequency (%)
080
19.9%
1199
49.4%
260
 
14.9%
336
 
8.9%
416
 
4.0%
511
 
2.7%
61
 
0.2%
ValueCountFrequency (%)
61
 
0.2%
511
 
2.7%
416
 
4.0%
336
 
8.9%
260
 
14.9%
1199
49.4%
080
19.9%

Volume_Monthly
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.848635236
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2021-06-15T15:52:35.818981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.323126432
Coefficient of variation (CV)0.7157314794
Kurtosis4.376103305
Mean1.848635236
Median Absolute Deviation (MAD)0
Skewness1.99014807
Sum745
Variance1.750663556
MonotonicityNot monotonic
2021-06-15T15:52:35.926821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1233
57.8%
283
 
20.6%
344
 
10.9%
418
 
4.5%
515
 
3.7%
65
 
1.2%
74
 
1.0%
91
 
0.2%
ValueCountFrequency (%)
1233
57.8%
283
 
20.6%
344
 
10.9%
418
 
4.5%
515
 
3.7%
65
 
1.2%
74
 
1.0%
91
 
0.2%
ValueCountFrequency (%)
91
 
0.2%
74
 
1.0%
65
 
1.2%
515
 
3.7%
418
 
4.5%
344
 
10.9%
283
 
20.6%
1233
57.8%

ReclameAqui
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size531.0 B
True
403 
ValueCountFrequency (%)
True403
100.0%
2021-06-15T15:52:36.026013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

SocialMedia
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size531.0 B
False
402 
True
 
1
ValueCountFrequency (%)
False402
99.8%
True1
 
0.2%
2021-06-15T15:52:36.067021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Tempo_Medio_Chat
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct272
Distinct (%)67.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1053.679204
Minimum0
Maximum6115.292
Zeros132
Zeros (%)32.8%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2021-06-15T15:52:36.175322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median955.7349999
Q31590.9052
95-th percentile2792.79305
Maximum6115.292
Range6115.292
Interquartile range (IQR)1590.9052

Descriptive statistics

Standard deviation1051.631075
Coefficient of variation (CV)0.9980562125
Kurtosis1.958354123
Mean1053.679204
Median Absolute Deviation (MAD)955.7349999
Skewness1.179288823
Sum424632.719
Variance1105927.918
MonotonicityNot monotonic
2021-06-15T15:52:36.323005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0132
32.8%
1840.8256671
 
0.2%
1230.3071
 
0.2%
2048.62051
 
0.2%
1635.3151
 
0.2%
1005.6591
 
0.2%
1573.32161
 
0.2%
672.0011
 
0.2%
277.83551
 
0.2%
1012.6581
 
0.2%
Other values (262)262
65.0%
ValueCountFrequency (%)
0132
32.8%
57.335000041
 
0.2%
77.963999991
 
0.2%
244.481
 
0.2%
277.83551
 
0.2%
354.49700011
 
0.2%
388.44300011
 
0.2%
406.8981
 
0.2%
414.87299991
 
0.2%
415.00099991
 
0.2%
ValueCountFrequency (%)
6115.2921
0.2%
5290.8231
0.2%
4814.8426671
0.2%
4390.1191
0.2%
4315.7821
0.2%
4248.6031
0.2%
3963.488751
0.2%
3957.7581
0.2%
3913.5771
0.2%
3711.2741
0.2%

Tempo_Medio_Email
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct179
Distinct (%)44.5%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean5541.277412
Minimum0
Maximum134313.7083
Zeros224
Zeros (%)55.6%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2021-06-15T15:52:36.485927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38231.020833
95-th percentile22376.76042
Maximum134313.7083
Range134313.7083
Interquartile range (IQR)8231.020833

Descriptive statistics

Standard deviation11020.25764
Coefficient of variation (CV)1.988757613
Kurtosis52.7919528
Mean5541.277412
Median Absolute Deviation (MAD)0
Skewness5.725167772
Sum2227593.519
Variance121446078.4
MonotonicityNot monotonic
2021-06-15T15:52:36.651194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0224
55.6%
4633.4444441
 
0.2%
24804.041671
 
0.2%
6721.8611111
 
0.2%
8594.1388891
 
0.2%
10092.251
 
0.2%
8672.5416671
 
0.2%
180371
 
0.2%
9325.751
 
0.2%
4741.9583331
 
0.2%
Other values (169)169
41.9%
ValueCountFrequency (%)
0224
55.6%
1634.0416671
 
0.2%
1891.2916671
 
0.2%
2111.4583331
 
0.2%
2172.2916671
 
0.2%
2525.2916671
 
0.2%
2568.6666671
 
0.2%
2639.8333331
 
0.2%
2643.5416671
 
0.2%
3079.2083331
 
0.2%
ValueCountFrequency (%)
134313.70831
0.2%
74088.833331
0.2%
65903.291671
0.2%
60704.229171
0.2%
38704.958331
0.2%
32962.51
0.2%
32014.51
0.2%
29908.083331
0.2%
28815.8751
0.2%
27559.895831
0.2%

AWT_Chat
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct174
Distinct (%)43.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.38726288
Minimum0
Maximum1981.021
Zeros132
Zeros (%)32.8%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2021-06-15T15:52:36.813027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median17
Q352.5
95-th percentile604.55
Maximum1981.021
Range1981.021
Interquartile range (IQR)52.5

Descriptive statistics

Standard deviation253.7486784
Coefficient of variation (CV)2.579080573
Kurtosis20.03363266
Mean98.38726288
Median Absolute Deviation (MAD)17
Skewness4.191011677
Sum39650.06694
Variance64388.39177
MonotonicityNot monotonic
2021-06-15T15:52:36.962684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0132
32.8%
67
 
1.7%
87
 
1.7%
196
 
1.5%
116
 
1.5%
256
 
1.5%
125
 
1.2%
245
 
1.2%
75
 
1.2%
175
 
1.2%
Other values (164)219
54.3%
ValueCountFrequency (%)
0132
32.8%
41
 
0.2%
51
 
0.2%
67
 
1.7%
6.51
 
0.2%
75
 
1.2%
7.51
 
0.2%
87
 
1.7%
8.3333333331
 
0.2%
8.51
 
0.2%
ValueCountFrequency (%)
1981.0211
0.2%
17501
0.2%
16911
0.2%
1243.20081
0.2%
12081
0.2%
1190.51
0.2%
11361
0.2%
10631
0.2%
10561
0.2%
10111
0.2%

%NFCR
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct13
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2571133168
Minimum0
Maximum1
Zeros233
Zeros (%)57.8%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2021-06-15T15:52:37.118927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.5
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.3447907169
Coefficient of variation (CV)1.341006842
Kurtosis-0.2892674134
Mean0.2571133168
Median Absolute Deviation (MAD)0
Skewness1.019136498
Sum103.6166667
Variance0.1188806384
MonotonicityNot monotonic
2021-06-15T15:52:37.241778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0233
57.8%
0.557
 
14.1%
143
 
10.7%
0.333333333325
 
6.2%
0.666666666717
 
4.2%
0.256
 
1.5%
0.45
 
1.2%
0.25
 
1.2%
0.755
 
1.2%
0.82
 
0.5%
Other values (3)5
 
1.2%
ValueCountFrequency (%)
0233
57.8%
0.25
 
1.2%
0.256
 
1.5%
0.333333333325
 
6.2%
0.45
 
1.2%
0.42857142862
 
0.5%
0.557
 
14.1%
0.57142857142
 
0.5%
0.61
 
0.2%
0.666666666717
 
4.2%
ValueCountFrequency (%)
143
10.7%
0.82
 
0.5%
0.755
 
1.2%
0.666666666717
 
4.2%
0.61
 
0.2%
0.57142857142
 
0.5%
0.557
14.1%
0.42857142862
 
0.5%
0.45
 
1.2%
0.333333333325
6.2%

%Insatisfação(CSAT)
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
0.0
320 
1.0
77 
0.5
 
3
0.3333333333333333
 
2
0.25
 
1

Length

Max length18
Median length3
Mean length3.076923077
Min length3

Characters and Unicode

Total characters1240
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0320
79.4%
1.077
 
19.1%
0.53
 
0.7%
0.33333333333333332
 
0.5%
0.251
 
0.2%

Length

2021-06-15T15:52:37.527108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-15T15:52:37.653886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0320
79.4%
1.077
 
19.1%
0.53
 
0.7%
0.33333333333333332
 
0.5%
0.251
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0723
58.3%
.403
32.5%
177
 
6.2%
332
 
2.6%
54
 
0.3%
21
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number837
67.5%
Other Punctuation403
32.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0723
86.4%
177
 
9.2%
332
 
3.8%
54
 
0.5%
21
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.403
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0723
58.3%
.403
32.5%
177
 
6.2%
332
 
2.6%
54
 
0.3%
21
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0723
58.3%
.403
32.5%
177
 
6.2%
332
 
2.6%
54
 
0.3%
21
 
0.1%

CSAT_Rated
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4789081886
Minimum0
Maximum5
Zeros247
Zeros (%)61.3%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2021-06-15T15:52:37.793503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7168385533
Coefficient of variation (CV)1.496818326
Kurtosis6.64620797
Mean0.4789081886
Median Absolute Deviation (MAD)0
Skewness2.049297749
Sum193
Variance0.5138575115
MonotonicityNot monotonic
2021-06-15T15:52:37.958948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0247
61.3%
1130
32.3%
219
 
4.7%
34
 
1.0%
42
 
0.5%
51
 
0.2%
ValueCountFrequency (%)
0247
61.3%
1130
32.3%
219
 
4.7%
34
 
1.0%
42
 
0.5%
51
 
0.2%
ValueCountFrequency (%)
51
 
0.2%
42
 
0.5%
34
 
1.0%
219
 
4.7%
1130
32.3%
0247
61.3%

Target
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
0
352 
1
51 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters403
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0352
87.3%
151
 
12.7%

Length

2021-06-15T15:52:38.320907image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-15T15:52:38.420953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0352
87.3%
151
 
12.7%

Most occurring characters

ValueCountFrequency (%)
0352
87.3%
151
 
12.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number403
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0352
87.3%
151
 
12.7%

Most occurring scripts

ValueCountFrequency (%)
Common403
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0352
87.3%
151
 
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII403
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0352
87.3%
151
 
12.7%

Interactions

2021-06-15T15:52:15.430103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:15.961842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:16.274770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:16.457823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:16.660709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:16.873112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:17.084020image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:17.340504image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:17.615777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:17.886905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:18.105935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:18.321922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:18.524923image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:18.695991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:18.860166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:19.036865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:19.202751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:19.375907image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:19.550816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:19.718883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:19.872770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:20.017910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:20.168944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:20.296887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:20.433785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:20.582833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:20.728637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:20.873977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:21.027032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:21.184947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:21.330775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:21.490811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:21.657826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:21.808865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:21.955951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:22.130007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:22.310044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:22.487910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:22.651795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:22.803888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:22.942951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:23.103737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:23.276841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:23.429120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:23.589149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:23.750162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:23.905177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:24.065275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:24.228697image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:24.379071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:24.527821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:24.682727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:24.857729image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:24.997784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:25.141779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:25.295132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:25.448868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:25.617817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:25.777184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:25.925234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:26.070330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:26.230010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:26.405182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:26.550850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:26.705835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:26.874075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:27.588298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:27.747899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:27.915407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:28.064824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:28.220948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:28.408863image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:28.605668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:28.781395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:28.963887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:29.150158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:29.315884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:29.479851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:29.646756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:29.794796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:29.952471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:30.100956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:30.270290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:30.420783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:30.577883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:30.740029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:30.891347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:31.049662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:31.210025image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:31.353063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:31.497962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:31.652919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:31.822819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:31.965833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:32.115752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:32.267878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:32.414834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:32.608827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:32.784258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:52:32.923973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-06-15T15:52:38.515233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-15T15:52:38.795202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-15T15:52:39.071954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-15T15:52:39.744175image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-06-15T15:52:40.080131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-06-15T15:52:33.179846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-06-15T15:52:33.563852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-06-15T15:52:33.735108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexRequester_IDMonthVolume_7daysVolume_15daysVolume_MonthlyReclameAquiSocialMediaTempo_Medio_ChatTempo_Medio_EmailAWT_Chat%NFCR%Insatisfação(CSAT)CSAT_RatedTarget
096101217352021_01122yesno0.0000008846.8750000.0000000.50.000
181111283222021_01011yesno0.00000023428.7083330.0000000.00.000
22134134811922021_01011yesno0.00000028815.8750000.0000000.00.000
32367138647772021_01111yesno0.00000011196.0000000.0000000.00.000
42398138875002021_01555yesno729.50825011261.95833326.5000000.20.030
53287152044122021_01344yesno1240.44566722395.66666732.6666670.50.021
64176165110022021_01555yesno800.0767503309.375000466.5007500.40.010
74473167372702021_01011yesno0.0000006550.3750000.0000001.01.010
84674169208282021_01011yesno0.0000008219.1666670.0000000.01.010
94925169858632021_01111yesno0.0000004444.2916670.0000000.00.010

Last rows

df_indexRequester_IDMonthVolume_7daysVolume_15daysVolume_MonthlyReclameAquiSocialMediaTempo_Medio_ChatTempo_Medio_EmailAWT_Chat%NFCR%Insatisfação(CSAT)CSAT_RatedTarget
3931466265138112021_05244yesno3963.488750.035.50.250.33333330
39414816662006992021_05011yesno1411.658000.0149.00.000.00000000
39514938171757222021_05002yesno3644.859500.093.50.500.00000000
39615059481919432021_05111yesno526.461000.078.00.000.00000010
39715081283661642021_05011yesno869.448000.088.00.000.00000000
39815106186135722021_05011yesno2189.555000.0607.00.001.00000010
39915121487775602021_05012yesno2440.290500.0428.50.500.00000000
40015173992377792021_05222yesno2631.868000.016.00.000.00000000
4011518809311642021_05111yesno1390.970000.028.00.000.00000000
40215213395320322021_05001yesno1508.047000.0713.00.000.00000010